1. Technical Field
The invention relates to the measurement of blood analytes. More particularly, the invention relates to an intelligent system for noninvasive blood analyte prediction.
2. Description of the Prior Art
The goal of noninvasive blood analyte measurement is to determine the concentration of targeted blood analytes without penetrating the skin. Near infrared (NIR) spectroscopy is a promising noninvasive technology which bases measurements on the absorbance of low energy NIR light that is transmitted into a subject. The light is focused onto a small area of the skin and propagates through subcutaneous tissue. The reflected or transmitted light that escapes and is detected by a spectrometer provides information about the tissue contents that it has penetrated.
The absorbance of light at each wavelength is a function of the structural properties and chemical composition of the tissue. Tissue layers, each containing a unique heterogeneous particulate distribution, affect light absorbance through scattering. Chemical components, such as water, protein, fat and blood analytes, absorb light proportionally to their concentration through unique absorption profiles or signatures. The measurement of blood analyte concentrations is based on detecting the magnitude of light attenuation caused by the absorption signature of the targeted analyte. The process of calibration is the development of a mathematical transformation or model which estimates the blood analyte concentration from the measured tissue absorbance spectrum.
However, accurate noninvasive estimation of blood analytes is presently limited by the dynamic nature of the sample, the skin and living tissue of the subject. Chemical, structural and physiological variations occur that produce dramatic changes in the optical properties of the tissue sample.
See, for example, R. Anderson, J. Parrish, The optics of human skin, Journal of Investigative Dermatology, vol. 77(1), pp. 13-19 (1981); W. Cheong, S Prahl, A. Welch, A review of the optical properties of biological tissues, IEEE Journal of Quantum Electronics, vol. 26(12), pp. 2166-2185 (December 1990); D. Benaron, D. Ho, Imaging (NIRI) and quantitation (NIRS) in tissue using time-resolved spectrophotometry: the impact of statically and dynamically variable optical path lengths, SPIE, vol. 1888, pp.10-21 (1993); J. Conway, K. Norris, C. Bodwell, A new approach for the estimation of body composition: infrared interactance, The American Journal of Clinical Nutrition, 40, pp. 1123-1140 (December 1984); S. Homma, T. Fukunaga, A. Kagaya, Influence of adipose tissue thickness in near infrared spectroscopic signals in the measurement of human muscle, Journal of Biomedical Optics, 1(4), pp. 418-424 (October 1996); A. Profio, Light transport in tissue, Applied Optics, vol. 28(12), pp. 2216-2222 (June 1989); and M. Van Gemert, S. Jacques, H. Sterenborg, W. Star, Skin optics, IEEE Transactions on Biomedical Engineering, vol. 36(12), pp. 1146-1154 (December 1989).
These variations include the following general categories:
1. Covariation of spectrally interfering species. The NIR spectral absorption profiles of blood analytes tend to overlap and vary simultaneously over brief time periods. This produces spectral interference and necessitates the measurement of absorbance at more independently varying wavelengths than the number of interfering species. PA1 2. Sample heterogeneity. The tissue measurement site has multiple layers and compartments of varied composition and scattering. The spectral absorbance versus wavelength is related to a complex combination of the optical properties and composition of these tissue components. Therefore, a general representation or model of the tissue absorbance spectrum is nonlinear and difficult to realize on the basis of first principles. PA1 3. State Variations. Variations in the subject's physiological state effect the optical properties of tissue layers and compartments over a relatively short period of time. Such variations, for example, may be related to hydration levels, changes in the volume fraction of blood in the tissue, hormonal stimulation, temperature fluctuations and blood hemoglobin levels. PA1 4. Structural Variations. The tissue characteristics of individuals differ as a result of factors that include hereditary, environmental influences, the aging process, sex and body composition. These differences are largely anatomical and can be categorized as slowly varying structural properties producing diverse tissue geometry. Consequently, the tissue of a given subject has distinct systematic spectral absorbance features or patterns that can be related directly to specific characteristics such as dermal thickness, protein levels and percent body fat. While the absorbance features are repeatable by subject, over a population of subjects they produce confounding nonlinear spectral variation. Therefore, differences between subjects are a significant obstacle to the noninvasive measurement of blood analytes through NIR spectral absorbance. In a nondispersive system, variations similar to (1) above are easily modeled through multivariate techniques, such as multiple linear regression and factor based algorithms. Significant effort has been expended to model the scattering properties of tissue in diffuse reflectance although the problem outlined in (2) above has been largely unexplored. Variations of the type listed in (3) and (4) above causes significant nonlinear spectral variation for which an effective solution has not been reported. For example, several reported methods of noninvasive glucose measurement develop calibration models that are specific to an individual over a short period of time. PA1 The first rule assumes that the classes are mutually exclusive and applies specific calibration models to the various subject categories. PA1 The second rule uses fuzzy set theory to develop calibration models and blood analyte predictions. Therefore, each calibration sample has the opportunity to influence more than one calibration model according to its class membership. Similarly, the predictions from more than one calibration are combined through defuzzification to produce the final blood analyte prediction.
See, for example, K. Hazen, Glucose determination in biological matrices using near-infrared spectroscopy, Doctoral Dissertation, University of Iowa (August 1995); J. Burmeister, In vitro model for human noninvasive blood glucose measurements, Doctoral Dissertation, University of Iowa (December 1997); and M. Robinson, R. Eaton, D. Haaland, G. Koepp, E. Thomas, B. Stallard, P. Robinson, Noninvasive glucose monitoring in diabetic subjects: a preliminary evaluation, Clin. Chem, 38/9, pp. 1618-1622 (1992).
This approach avoids modeling the differences between subjects and therefore cannot be generalized to more individuals. However, the calibration models have not been tested over long time periods during which variation of type (4) above may require recalibration. Furthermore, the reported methods have not been shown to be effective over a range of type (3) above variations.
It would be desirable to provide a method and apparatus for compensating for the variations described above.